Crypto AI Analytics is a sophisticated desktop tool designed for data-driven crypto market analysis and intelligent trend detection.

A robust, regime-adaptive QQQ trading strategy utilizing ensemble machine learning and options market microstructure signals (GEX, VRP, Skew).

End-to-end algorithmic trading system using PyTorch LSTM. Features a custom Cross-Sectional Alpha ranking engine, dynamic portfolio compounding, and event-driven backtesting (+483% return vs SPY).

A Topological Data Analysis (TDA) based trading framework that models financial markets as geometric manifolds to detect persistent structural trends using Betti-0 connectivity, enabling robust alpha generation under real-world execution constraints.

A Python trading bot that combines momentum, mean reversion, and volatility signals to run a long-short strategy. Includes a custom walk-forward optimizer to automatically tune the model as market conditions change.

Strat-ML: S&P 500 Alpha Generation Framework:- This repository contains a complete quantitative pipeline designed to outperform the S&P 500 Index using Machine Learning. The project focuses on out-of-sample signal generation using constituent-level OHLCV data, rigorous Blocking Time Series Cross-Validation, and a Long/Short Stock Picking strategy.

A quantitative framework modeling recursive interactions between technology, capital, and delayed information absorption.

A comprehensive, production-grade implementation of quantitative trading strategies across 18 asset classes. Features modular Python architecture, institutional risk management, and deep-dive research papers for every strategy.

An open-source framework for autonomous digital entities, focused on decentralized threat & alpha intelligence.